code: def buffer_operation(func):
      def remove_creator(tensors):
          if not tensors:
              return
  
          if isinstance(tensors, Iterable):
              if isinstance(tensors, str):
                  return
              else:
                  for item in tensors:
                      if isinstance(item, Iterable):
                          remove_creator(item)
                      elif isinstance(item, tensor.Tensor):
                          item.creator = None
          elif isinstance(tensors, tensor.Tensor):
              tensors.creator = None
  
      @wraps(func)
      def wrapper(self, *args, **kwargs):
          if self.graph_mode and self.training:
              if len(args) == 0:
                  raise ValueError("expect at least one input tensor")
  
              if isinstance(args[0], list):
                  assert isinstance(
                      args[0][0], Tensor
                  ), "function expects PlaceHolders or Tensors"
                  dev = args[0][0].device
              else:
                  assert isinstance(
                      args[0], Tensor
                  ), "function expects PlaceHolders or Tensors"
                  dev = args[0].device
  
              if not self._buffered:
                  # buffer operations
                  dev.EnableGraph(True)
                  self._results = func(self, *args, **kwargs)
                  dev.Sync()
                  dev.EnableGraph(False)
                  self._buffered = True
  
                  # deconstruct Operations before running the entire graph
                  remove_creator(self._results)
  
                  # make sure all Operations are deallocated
                  gc.collect()
  
              # run graph
              dev.RunGraph(self.sequential)
              return self._results
          else:
              return func(self, *args, **kwargs)
  
      return wrapper
  
imports: import os
  import gc
  import time
  import json
  import zipfile
  import numpy
  from functools import wraps
  from collections import Iterable
  from singa import tensor
  from singa import autograd
  from singa import layer
  from .tensor import Tensor
  from .None import singa_wrap
label_desc: Use image node_id 1 for calling the ModelMeta obj's underlying member method code with example usage: obj.buffer_operation(func) and returns: wrapper, self, func